Rock-Paper-Scissors-Image-Classification

This Rock Paper Scissor Detector

Notebook

https://colab.research.google.com/drive/1tUXYwDSpNvQwfbJZ4ZAFSmL2oPG0242d?usp=sharing

Model Architecture

The model is a Convolutional Neural Network (CNN) with the following layers:

• 4 Convolutional layers
• MaxPooling layers
• A Dense (fully connected) layer with 512 neurons
• Output layer with 3 neurons (for 3 classes: rock, paper, scissors)

The model uses the adam optimizer and is compiled with the categorical crossentropy loss function.

Training

The data is augmented using Keras's ImageDataGenerator to improve generalization. The model is trained for 12 epochs with early stopping to prevent overfitting.'

Training Script

history = model.fit(
    train_generator,
    epochs=15,
    validation_data=val_generator,
    callbacks=[early_stopping],
    verbose=2
)

Result

After training, the model achieved the following performance:

• Training Accuracy: ~98.86%
• Validation Accuracy: ~97.60%

Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue for any suggestions or improvements.